Companies are racing to adopt AI, but too many are measuring the wrong thing: whether people are using the tools, rather than whether the business is actually getting better. Boards and executives should demand evidence that AI improves productivity, strengthens judgment, preserves institutional knowledge, and creates measurable financial value, not simply evidence that employees are logging in. This matters far beyond technology departments.
AI is already changing how people work, how customers interact with companies, and how leaders allocate capital. According to Boston Consulting Group (BCG), 50 to 55 percent of U.S. jobs could be reshaped by AI within the next two to three years, while 10 to 15 percent could potentially be eliminated over the longer term. The central business question is not whether work will change.
It is whether companies will manage that change intelligently. I worry that many are starting at the wrong end. Leaders buy a tool because AI is fashionable, then ask employees to find something to do with it.
AI should begin with a business problem, a baseline, and a definition of success. The distinction is critical because automating a task does not automatically improve a business. Saving five hours is meaningless if those hours simply disappear into more meetings.
A faster report is not valuable if it contains more errors. And reducing headcount based on expected productivity gains can destroy institutional knowledge before those gains ever materialize. I have seen AI work beautifully when it is treated as a thought partner rather than an oracle.
In financial modeling, for example, I want humans to build the core model, understand the assumptions, and challenge the business logic. AI can then help test formulas, incorporate complex updates, summarize changes, and accelerate iteration. The technology makes the work faster; human judgment makes it useful.
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